US8775154B2 - Query translation through dictionary adaptation - Google Patents
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- G06—COMPUTING; CALCULATING OR COUNTING
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- G06F16/30—Information retrieval; Database structures therefor; File system structures therefor of unstructured textual data
- G06F16/33—Querying
- G06F16/3331—Query processing
- G06F16/3332—Query translation
- G06F16/3337—Translation of the query language, e.g. Chinese to English
Definitions
- the following relates to the information processing arts, information retrieval arts, cross-lingual natural language processing arts, and related arts.
- Information retrieval systems provide a user-friendly interface by which a user can retrieve documents from a database that are relevant to or match a query.
- an information retrieval system ranks a “top N” documents that best match the query.
- An example of such a system is an Internet search engine.
- the information retrieval can operate by identifying documents of the database that contain the same words as those specified in the query.
- the query “President Clinton” retrieves documents that include the terms “President” and “Clinton”.
- this approach does not facilitate ranking; for example, if each of five different documents contain all words of the query, then there is no mechanism by which they can be relatively ranked. It may also fail if a relevant document contains most, but not all, terms of a query.
- a language model is generated to represent the distribution of vocabulary terms in a document.
- One language model is P ML (w
- the language model is typically smoothed, for example P(w
- d) ⁇ P ML (w
- C) is the maximum-likelihood language model for the corpus C representing a statistical distribution or likelihood of term w in the corpus C
- d) is the smoothed language model
- A controls the amount of smoothing.
- Such smoothing ensures a non-zero occurrence language model probability for vocabulary terms that do not occur in the document d.
- d) are the outputs of the language model of document d for query terms q i .
- a known extension is pseudo-relevance feedback.
- a first retrieval operation is performed to obtain N most relevant documents, where N is an integer and N>0.
- a language model is derived for the N documents, and is used to update or enrich the original query. The idea is that the N most relevant documents are likely to be highly related to the subject matter of the query, and so similarities amongst the N most relevant documents provide information for enriching the query.
- vocabulary terms that were not included in the original query but are highly probable in the language model of the top-N documents may be added to the original query to generate an improved query that is used in a second retrieval operation.
- the foregoing relates to mono-lingual information retrieval, in which the query and the corpus of documents are both in the same language.
- Cross-lingual information retrieval systems extend this concept by retrieving documents from a corpus of documents in a target natural language based on a query formulated in a different source natural language.
- the query may be formulated in English, but the corpus of documents may be in German.
- Cross-lingual information retrieval facilitates retrieval of documents from a multi-lingual corpus (for example, containing documents in English, French, and German) using a single source-language query (for example, a query in English).
- a multi-lingual corpus for example, containing documents in English, French, and German
- a single source-language query for example, a query in English.
- cross-lingual information retrieval enables a person not fluent in the language of a mono-lingual corpus to formulate a query in his or her native language, and thus to retrieve the most relevant documents. The user can then obtain human and/or machine translations only of the retrieved most relevant documents.
- Cross-lingual information retrieval can be performed by translating the query from the source language into the target language and performing the query on the documents in the target language using the translated query. That is, the query is moved into the target language domain and henceforth the retrieval system operates in the target language domain. This leverages the monolingual indexing and retrieval machinery of the corpus, and entails only constructing a “front end” to perform the query translation.
- cross-lingual information retrieval is dependent upon accurate translation of the query from the source language to the target language. This can be difficult due to the possibly short length of the query and generally known difficulties in automated translation between different natural languages.
- Cross-lingual dictionaries are sometimes automatically generated as lexicons extracted from parallel corpora (that is, a corpus in which the same semantic textual content is represented in both the source and target languages). Such automatically generated dictionaries can contain errors due to misalignment of nominally parallel text, uncertainties in the extraction processes, and other errors introduced in the automated lexicon generation. They are also typically noisy, because they are automatically extracted from an aligned corpus. Noise can be introduced by mistakes in extraction algorithm (for example, the extraction of entries is computed with an alignment between words and reliance upon statistics). Noise can also be introduced when a dictionary is used in another domain.
- pseudo-relevance feedback is an attractive possibility for overcoming such translational difficulties, since the feedback documents provide potentially relevant contextual information.
- adapting pseudo-relevance feedback to cross-lingual information retrieval has heretofore been problematic.
- one approach that has been attempted is to build language models of the target-language corpus documents in the source language, and to perform mono-lingual information retrieval including pseudo-relevance feedback using those source-language models.
- this approach is computationally intensive, and has been found to generate unacceptably noisy feedback.
- a method of performing cross-lingual information retrieval comprising: translating a query in a source natural language into a target natural language different from the source natural language to generate a starting query in the target natural language; performing a first information retrieval operation on a corpus of documents in the target natural language using the starting query in the target natural language to retrieve a set of pseudo-feedback documents in the target natural language; generating an updated query in the target natural language based on the set of pseudo-feedback documents in the target natural language; and performing a second information retrieval operation on the corpus of documents in the target natural language using the updated query in the target natural language to retrieve an updated set of documents in the target natural language.
- a cross-lingual information retrieval system comprising a monolingual information retrieval system configured to retrieve documents from a corpus of documents in a target natural language based on a received query in the target natural language, and a processor configured to add cross-lingual information retrieval capability to the monolingual information retrieval system.
- the processor performs a process including: translating a query from a source natural language into the target natural language and inputting the translated query to the monolingual information retrieval system to retrieve a set of pseudo-feedback documents in the target natural language; generating a source language to target language translation model based on the set of pseudo-feedback documents; and re-translating the query from the source natural language into the target natural language using the translation model and inputting the re-translated query to the monolingual information retrieval system to retrieve an updated set of documents in the target natural language.
- a storage medium stores instructions executable to perform a cross-lingual information retrieval method comprising: translating a received query from a source natural language into a target natural language; performing a first information retrieval operation on a corpus of documents in the target natural language using the translated query to retrieve a set of pseudo-feedback documents in the target natural language; re-translating the received query from the source natural language into the target natural language using a translation model derived from the set of pseudo-feedback documents in the target natural language; and performing a second information retrieval operation on the corpus of documents in the target natural language using the re-translated query to retrieve an updated set of documents in the target natural language.
- FIG. 1 diagrammatically shows a cross-lingual information retrieval system incorporating pseudo-relevance feedback to simultaneously enrich the query and to improve query translation accuracy.
- a cross-lingual information retrieval system is constructed based on a monolingual information retrieval system 4 that retrieves information from a monolingual corpus 6 containing documents in a target natural language.
- the term “document” is intended to encompass any information collection or grouping that includes at least some text in a natural language, such documents including but not limited to: word processing documents or portions thereof; text-searchable PDF documents or portions thereof; XML documents or portions thereof; HTML documents or portions thereof; spreadsheet documents or portions thereof; presentation documents or portions thereof; electronic mail (email) messages; or so forth.
- the documents in the monolingual corpus 6 include text in the target natural language.
- the monolingual corpus 6 may be a standalone corpus containing only documents in the target language. For example, if the target language is French, then the monolingual corpus 6 may comprise a standalone corpus containing only French-language documents. Alternatively, the monolingual corpus 6 may be a sub-set of a multi-lingual corpus. For example, the monolingual corpus 6 may include the French-language documents of a multi-lingual corpus including (for example) English-, French-, and German-language documents. Still further, it is contemplated for the monolingual corpus 6 to include documents containing text in the target language as well as in one or more additional natural languages. Thus, for example, the monolingual corpus 6 having French as the target natural language may include technical manuals having parallel sections written in English, French, and German.
- the monolingual information retrieval system 4 is configured to receive a query in the target natural language and to retrieve documents from the monolingual corpus 6 in the target natural language based on the received query. In other words, the monolingual information retrieval system 4 operates in the target language domain on the corpus 6 which contains documents in the target language domain.
- the monolingual information retrieval system 4 can employ substantially any suitable monolingual information retrieval method.
- d) ⁇ P ML (w
- the documents can be ranked according to a suitable measure of similarity between a document and the query in the target natural language.
- this similarity measure can be represented as P(q
- q) may be set to P(w
- q) 1 ⁇ w ⁇ q 1 , . . . , q l ⁇ and to P(w
- q) 0 otherwise.
- ⁇ q the query language model
- one suitable ranking value for a document d is given by a cross-entropy measure between the query language model ⁇ q and the document language model ⁇ d , that is,
- ⁇ d ) ⁇ w ⁇ ⁇ qw ⁇ log ⁇ ( ⁇ dw ) .
- Other ranking measures such as a KL-divergence-based ranking measure, are also contemplated.
- the monolingual information retrieval system 4 can be any system configured to (i) receive as input a query in the target natural language and to (ii) output a set of documents retrieved from the corpus 6 that are relevant to the received query.
- the cross-lingual information retrieval system of FIG. 1 utilizes the monolingual information retrieval system 4 to perform cross-lingual information retrieval, and further incorporates pseudo-relevance feedback to simultaneously enrich the query and to improve query translation accuracy.
- a query 10 is received in a source natural language that is different from the target natural language of the monolingual information retrieval system 4 .
- a translation engine 12 translates the query 10 into the target natural language.
- the translation engine 12 makes use of a bilingual dictionary 14 .
- the bilingual dictionary 14 is a data compilation capable of receiving as input a vocabulary term in the source natural language and outputting one or more potential translations which are vocabulary terms in the target natural language that are potential translations for the input source natural language vocabulary term.
- the bilingual dictionary 14 optionally may also be operative in the reverse direction, that is, may be capable of receiving as input a vocabulary word in the target language and outputting one or more words in the source language as possible translations.
- the output of the translation engine 12 is a starting query 16 in the target natural language.
- the starting query 16 may have certain deficiencies. For example, if an input source natural language vocabulary term of the source-language query 10 is polysemic, then the bilingual dictionary 14 will output two or more potential translations, both (or all) of which will be included in the starting query 16 . Moreover, any deficiencies of the source-language query 10 , such as failure to include certain terms that may be relevant to the subject matter of the query, will carry through the translation into the translated starting query 16 .
- a first retrieval operation 20 the monolingual information retrieval system 4 is invoked to retrieve from the corpus 6 a set of ranked documents relevant to the translated starting query 16 .
- the output of the first retrieval operation 20 is treated as a set of pseudo-feedback documents 22 in the target natural language, and are used to improve upon the translated starting query 16 .
- a query terms translation model generator 24 receives the set of pseudo-feedback documents 22 , and uses contextual information provided by the set of pseudo-feedback documents 22 to generate an improved translation model for translating the query terms of the source-language query 10 into the target natural language.
- the relative frequencies of the alternative translations of the polysemic term in the set of pseudo-feedback documents 22 can be measured and this information can be used to bias the translation of the polysemic term toward the more frequently occurring alternative translation or translations.
- the improved translation model optionally biases or weights the translations of different source language query terms relative to one another to reflect their frequency of occurrence in the set of pseudo-feedback documents 22 . For example, if a first source query word w s1 translates to target query word w d1 , and a second source query word w s2 translates to target query word w d2 , and the frequency of w d1 in the set of pseudo-feedback documents 22 is substantially higher than the frequency of w d2 , then this can be reflected by assigning a higher translation probability to the (w s1 , w d1 ) translation pair as compared with the (w s2 , w d2 ) translation pair.
- a query updater 26 re-translates the source-language query 10 to generate an updated query 28 in the target natural language.
- the monolingual information retrieval system 4 is invoked a second time to retrieve from the corpus 6 an updated set of ranked documents 32 in the target natural language, which this time are relevant to the re-translated query 28 .
- the updated or re-translated query 28 is expected to be substantially improved over the starting translated query 16 .
- polysemic source query terms are generally more accurately represented in the updated or re-translated query 28 as compared with in the starting translated query 16 .
- the updated or re-translated query 28 optionally weights the translations of different source query terms based on their frequency of occurrence in the set of pseudo-feedback documents 22 , which again is generally expected to enhance the representativeness of the updated or re-translated query 28 respective to the subject matter of the originally received source-language query 10 .
- the updated set of ranked documents 32 in the target natural language is taken as the final output set of documents, and is utilized in a suitable fashion.
- the updated set of ranked documents 32 may be displayed on a display D of a computer C or otherwise output in a human-perceptible format.
- the updated set of ranked documents 32 in the target natural language may first be translated to the source natural language by a machine translation engine 34 (which in some embodiments is identical with the translation engine 12 ), and the translated documents may be displayed on a display D of a computer C or otherwise output in a human-perceptible format. This latter approach is particularly suitable when the user is likely to be fluent in the source natural language but not in the target natural language.
- the updated set of ranked documents 32 is treated as an updated set of pseudo-feedback documents 22 , and are processed by the query terms translation model generator 24 and the query updater 26 to generate a further updated query in the target natural language that is used in a third invocation of the monolingual information retrieval system 4 to generate a further updated set of documents.
- This iterative processing is optionally performed one or more times to iteratively refine the query and the set of retrieved documents.
- the monolingual information retrieval system 4 is a standalone component, and a processor (such as the processor of the illustrated computer C) is configured to add cross-lingual information retrieval capability to the monolingual information retrieval system 4 by implementing the additional components 12 , 24 , 26 and including suitable hooks or links to the monolingual information retrieval system 4 to implement the retrieval operations 20 , 30 .
- the same processor may implement both the monolingual information retrieval system 4 and the additional components 12 , 24 , 26 .
- one processor may implement the monolingual information retrieval system 4 while another, different processor may implement the additional components 12 , 24 , 26 .
- the two different processors may be located remote from one another—for example the monolingual information retrieval system 4 could be an Internet search engine server which the processor implementing the additional components 12 , 24 , 26 accesses via the Internet.
- the various processing components 4 , 12 , 24 , 26 may be embodied as any suitable processing device, such as the illustrated computer C, or as a network server, or as a personal data assistant (PDA), or so forth.
- the query 10 may be received via various mechanisms, such as an illustrated keyboard K, a mouse, trackball, or other user input device, or the query may be received from an automated source such as an automated data mining system.
- the various processing components 4 , 12 , 24 , 26 may also be embodied as a storage medium storing instructions executable to perform the processing.
- Suitable storage media include, for example: magnetic tape; a magnetic disk or disks; an optical disk; random access memory (RAM); read-only memory (ROM); FLASH or other electrostatic memory; or so forth.
- the query terms translation model generator 24 receives the source language query 10 and the set of pseudo-feedback documents 22 .
- the query terms translation model generator 24 optionally receives further relevant information, such as an indication of the alternative target language translations for the various source-language query terms as provided by the bilingual dictionary 14 .
- This can also be represented as a probabilistic query language model according to p(w s
- q s ) 1, w s ⁇ w s1 , . . . , w sl ⁇ and p(w s
- q s ) may also have values intermediate between 0 and 1, such that p(w s
- the objective is to identify P(w t
- w s , q) which is the probability that the query term w s in the source language translates to the target language term w t , given the context of the query q.
- Computing this probability entails defining or approximating the context of a query, or its associated “concept”.
- the context of the query as it is initially given in the source language can be extracted from the set of pseudo-feedback documents 22 . In other words, the coherence is implicitly present in the set of pseudo-feedback documents 22 .
- d) ⁇ P ML (w
- C) as already discussed. Indexing the set of pseudo-feedback documents 22 as d i where i 1, . . .
- the document d i can be represented as a bag-of-words according to d i ⁇ Multinomial(l d i , ⁇ w s ⁇ s p(w s ,q s )+(1 ⁇ )p( ⁇
- ⁇ s can be interpreted as an adapted probability of translation, having vector elements ⁇ st ⁇ p(w t
- the parameter ⁇ s can also be interpreted as a probability distribution (multinomial parameter) over the vocabulary of target terms ⁇ w t ⁇ .
- the parameter ⁇ s is like a language model, but associated to a specific word w s of the initial source-language query q s .
- the role of the bilingual dictionary 14 is seen as providing reasonable starting values for the translation probabilities ⁇ st ⁇ p(w t
- an expectation-maximization (EM) algorithm is used to improve the translation probabilities ⁇ st ⁇ p(w t
- EM expectation-maximization
- the parameters ( ⁇ s ) w s ⁇ q are suitably estimated using a maximum likelihood approach using an EM-like algorithm in an operation 40 shown in FIG. 2 that is performed by the query terms translation model generator 24 .
- the parameter ⁇ which dictates the amount of smoothing is taken to be a known fixed parameter.
- q s ) is also known since it represents the distribution of words w s in the query q s 10 which is received in the source natural language.
- t wd represent the hidden random variable whose value is 1 if word w t in document d k has been generated by P( ⁇
- the subscript t is omitted to simplify the notation where the context makes clear that the expressions are in the target natural language domain, for example writing t wd instead of t w t d k ).
- ⁇ (0) is given by the corresponding part of an initial (probabilistic) non-adapted dictionary, that is, the starting query in the natural language 16 .
- the output of the iterative EM estimation is a set of adjusted source word-to-target word translation parameters ⁇ st ⁇ p(w t
- the term “maximize” and similar terms is intended to be broadly construed to encompass not only absolute maximization respective to a global maximum value, but also approximate maximization or maximization respective to a local maximum.
- an iterative process such as expectation maximization (EM) may be used to maximize a likelihood.
- the EM algorithm may be terminated based on stopping criteria that causes the EM algorithm to stop the maximization at a point where the likelihood is not yet at the absolute global maximum.
- maximization is said to maximize the likelihood, even though the final likelihood may not be the absolute largest likelihood attainable.
- the EM algorithm may converge to a local maximum rather than to the global maximum. Again, such maximization is said to maximize the likelihood, even though the final likelihood may not be the absolute largest likelihood attainable.
- the illustrative embodiment of FIG. 2 generates the adjusted source word-to-target word translation parameters ⁇ st ⁇ p(w t
- other techniques can be employed to maximize the likelihood of Equation (1).
- other likelihood formulations can be employed instead of the illustrative likelihood P(F
- other approaches besides likelihood maximization can be used to adjust the source word-to-target word translation parameters ⁇ st ⁇ p(w t
- w s ,q s ) 42 are used by the query updater 26 to update the query in the target natural language.
- the query updater 26 performs a query re-translation operation 44 , using the adjusted translation parameters 42 , to generate the updated query 28 in the target natural language.
- the probabilistic nature of the employed language model inherently biases toward those translation terms best matching the context provided by the set of pseudo-feedback documents 22 . Additionally, that same probabilistic nature ensures that the various query terms in the re-translated query are relatively weighted to emphasize terms that occur most frequently in the context provided by the set of pseudo-feedback documents 22 . In effect, the re-translation makes use of an adjusted dictionary learnt by the feedback processing 40 .
- the adjusted query is suitably represented by a language model p(w t
- this can be written as:
- Equation (1) The output of the re-translation 44 performed (in the illustrative embodiment) in accordance with Equation (7) is the updated query p(w t
- the starting query 16 translated using the dictionary 14 is used as the starting point for the EM algorithm.
- Operation of the EM algorithm ensures that any parameter value that starts at zero will remain at zero. In other words, the EM algorithm cannot cause a parameter initially having a value of zero to change to a value other than zero. As a consequence, only non-zero entries are affected by the EM processing. It follows that no new source word-to-translation word pairs are created by the EM algorithm.
- the (non-zero) dictionary weights are adapted to fit the feedback documents 22 and hence to choose an improved translation of the received query 10 .
Abstract
Description
where P(qi|d) are the outputs of the language model of document d for query terms qi.
Other ranking measures, such as a KL-divergence-based ranking measure, are also contemplated.
where k indexes the pseudo-feedback documents dk and c(wt|dk) is the number of (that is, count of) occurrences of the target-language term wt in the document dk.
where the index i denotes the current iteration of the EM algorithm. Then, as rws is defined only for twd=0 it follows that:
p(r ws=1|F,θ (i) ,t wd=0)∝p(w s |q s)θts (i) (4).
In the “M” step of the EM algorithm, it is desired to optimize a lower bound of the expected log-likelihood according to:
Differentiating with respect to θ(i+1) and adding Lagrange multiplier (for Σw
As already mentioned, θ(0) is given by the corresponding part of an initial (probabilistic) non-adapted dictionary, that is, the starting query in the
where the notation θst adapted emphasizes that the translation probability terms θst adapted≡p(wt|ws,qs) are probabilities obtained by maximizing the illustrative likelihood P(F|θ) given in Equation (1). The output of the re-translation 44 performed (in the illustrative embodiment) in accordance with Equation (7) is the updated query p(wt|qs) 28 in the target natural language.
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